class_docstr: Encapsulates fields and methods for RL policy and value function estimation with shared parameters
docstr: Compute value estimate(s) given the observation(s)
  
  Parameters
  ----------
  observation : np array
      Observation data (either single or a batch)
  **extra_feed
      Additional data such as state or mask (names of the arguments should match the ones in constructor, see __init__)
  
  Returns
  -------
  Value estimate
docstr_tok: ["Compute","value","estimate","(","s",")","given","the","observation","(","s",")","Parameters","--","--","--","--","--","observation",":","np","array","Observation","data","(","either","single","or","a","batch",")","*","*","extra_feed","Additional","data","such","as","state","or","mask","(","names","of","the","arguments","should","match","the","ones","in","constructor",",","see","__init__",")","Returns","--","--","--","-","Value","estimate"]
code: def value(self, ob, *args, **kwargs):
      """
      Compute value estimate(s) given the observation(s)
  
      Parameters
      ----------
      observation : np array
          Observation data (either single or a batch)
      **extra_feed
          Additional data such as state or mask (names of the arguments should match the ones in constructor, see __init__)
  
      Returns
      -------
      Value estimate
      """
      return self._evaluate(self.vf, ob, *args, **kwargs)
  
file_path: nni/nni/algorithms/hpo/ppo_tuner/policy.py
imports: import tensorflow.compat.v1
  from .distri import CategoricalPdType
  from .util import lstm_model, fc, observation_placeholder, adjust_shape
label_desc: Use image node_id 5 for calling the PolicyWithValue obj's underlying member method code with example usage: obj.value(ob) and returns: self